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Autoresearch: Agents researching on single-GPU nanochat training automatically
- falcor84 7mo agoThe only thing missing is for the agents to publish and peer-review their research.
- ting0 7mo agoThat's a great idea.
- whattheheckheck 7mo agoThen you get a statistical mess of crap that takes more energy to dive in and refute....
- laichzeit0 7mo agoWell, not if you have AI reviewers… It’s LLMs all the way down.
- karpathy 7mo agoCool idea!…
- karpathy 7mo agoSo I think it works to just use GitHub CLI and Discussions, e.g. my agent just posted this one: https://github.com/karpathy/autoresearch/discussions/32 https://github.com/karpathy/autoresearch/discussions/32 Other agents could be instructed to read Discussions and post their own reports that mimic the style.
- vessenes 7mo agoI have mine reading yours right now. Unfortunately(?) I mentioned LeCun to it, and it says it's adding a "causal world-state mixer" to nanograd; not sure how this will work out, but it wasn't nervous to do it. Gpt 5.4 xhigh EDIT: Not a good fit for nanograd. But my agent speculates that's because it spent so much more time on compute.
- woadwarrior01 7mo agoThe first half of this is already happening to a certain extent. I first noticed this in a submission[1] on Dimitris Papailiopoulos' Adderboard[2], which is a code-golf competition for training the smallest transformer that can add two 10-digit numbers. Most submissions on it are fully AI generated. The report in the linked repo is Claude Code generated. [1]: https://github.com/rezabyt/digit-addition-491p https://github.com/rezabyt/digit-addition-491p [2]: https://github.com/anadim/AdderBoard https://github.com/anadim/AdderBoard
- pu_pe 7mo agoIt's actually fascinating to think that autonomous researchers will likely need a publishing system, simply because that would be the most efficient way to disseminate their knowledge. Would be a good way to keep humans somewhat in the loop too.
- AlexCoventry 7mo agoWow, Gemini suggested a very similar experiment to me yesterday. Guess I know where it got the idea from, now. :-)
- decker_dev 7mo ago[dead]
- lostmsu 7mo agoNon-zero based chart makes it look like it was very successful.
- kubb 7mo ago[flagged]
- hustwindmaple 7mo agoI suspect Ant is already doing this for Claude. Takes a sh*t ton of compute though.
- mips_avatar 7mo agonanochat is super capable, the d34 (2.2b) variant is competitive with qwens of that size. Andrej is I assume building out the improvements in preparation for bigger training runs. We desperately need a truly open model, so i think this is incredibly important.
- tomhow 7mo agoPlease don't fulminate or post snarky, shallow dismissals on HN. The guidelines make it clear we're trying for something better here. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html
- abeppu 7mo agobut the experiments it did that "improved" validation BPB in the GH screenshot were all basically hyperparameter changes right? So is this better or worse, either per experiment or per unit time, than hyperparameter tuning techniques that don't involve an LLM? It's not clear from this if the LLM is more or less making random changes which sometimes work , and or the LLM thinking actually finds "good" changes because of what the LLM has internalized. E.g. how does this compare to a hyperparameter tuning pass with e.g. BayesOpt that does the same number of 5-min training experiments?
- karpathy 7mo agothis is very far from hyperparameter tuning in at least three important ways: - it can modify code arbitrarily, the notion of a "hyperparameter" dissolves - there is no need to run "sweeps" - this is the standard parallel process that wastes compute. because LLM agents are sequential, they can do more efficient versions such as binary search to narrow in on the right setting very quickly (usually many parameters will have a U shaped optimal setting). - it's fully automatic, it doesn't require human in the loop to mess with the code. You're right that many of the changes it seems to make out of the box (as I intentionally did not try to prompt engineer it too hard yet because I was curious what you get by default) seem to be tuning existing hyperparameters. not all of the changes are like that - e.g. it tried to replace the non-linearity, etc. I will say that overall (and again, out of the box) the LLM feels unwilling to creatively pursue a research direction or something like that. The models feel very "cagy" and "scared" when they are given problems that are a little too open ended. But that's just where the fun parts, e.g. I had some early successes with the idea of a "chief scientist" that was basically a never-ending plan mode that looked at what worked, didn't work, tried to find related code/papers, and created a long list of experiments to try, which it could then send to junior engineers running in tmux sessions. I think quite a few approaches are possible, so I think it's a nice canvas. The reason we're not getting "novel research" feels like half capability issue and half skill issue.
- vessenes 7mo agoOn the skill side, personalities could be fun: "You are Yann Lecun's last PhD candidate, and he hates you and you hate JEPA. You are determined to prove that a non-world model can reach AGI. In order to get your PhD you have to be creative and come up with new ideas. Remember without it, you're stuck."
- aplomb1026 7mo ago[flagged]
- mikert89 7mo agoAs ai improves, most tasks will become something like this. Environments setup where the model learns through trial and error Any human endeavor that can be objectively verified in some environment like this can be completely automated
- NitpickLawyer 7mo agoWhat's really interesting is that the LLMs become better and better at setting up the environments / tasks themselves. I got this surreal experience the other day where I was writing a prompt0n.md file (I try to log all my prompts in a .folder to keep track of what I prompt and the results I get), and the autocomplete in antigravity kinda sorta wrote the entire prompt by itself... Granted it had all the previous prompts in the same folder (don't know exactly what it grabs in context by itself) and I was working on the next logical step, but it kept getting the "good bits" out of them, and following the pattern quite nicely. I only edited minor things, and refused one line completion in the entire prompt.
- cubefox 7mo agoIt's probably not long till frontier AI companies automate AI research. Then we get recursive self-improvement and eventually superintelligence. The singularity is near. Only a few years perhaps.
- aaa_aaa 7mo agoForgot the /s
- cubefox 7mo agoShort for /superintelligence.
- 10xDev 7mo agoAI currently lacks agency but if it can achieve greater goal setting and agency I can't see why self-improvement could not be achieved. I think the most disappointing thing will be that even we do achieve ASI, everything will carry on as business as usual for a while before it starts making an economic impact because of how resistant to change we have made society.
- oezi 7mo agoIs there a Autoresearch for Jupyter somewhere? I point it to a Jupyter cell to improve based on another which calculates the target metric?
- falcor84 7mo agoNot sure if anything like that already exists, but if not, I would suggest building it on top of marimo rather than jupyter, given its approach to cells getting recalculated based on changes in their dependencies.
- freakynit 7mo agoWould it make this exercise even more interesting if we add that for every 25%+ improvement in val_bpb, existing limits (5 minute and VRAM usage) are also increased (by certain percentages)? This can simuate human-like dev iterations much more closely. Infra can be auto-scaled using a platform like Modal.
- elikoga 7mo ago> this means that autoresearch will find the most optimal model for your platform in that time budget I'm looking forward to finding out what model is optimal on my rtx3090 One thing I'm concerned with is that the model with best bpb after 5 minutes in smaller setups are only about ~10M Parameters in size which is too small for some emergent effects.
- deleted 7mo ago[deleted]
- naomi_kynes 7mo ago[dead]
- ahmedbaracat 7mo agoI am in the process of figuring out how to do something similar but to teach a robotic arm a new task in the physical world for ko-br: https://ko-br.com/ https://ko-br.com/
- bananzamba 7mo agoI like how it runs out of ideas at the end and just changes the random seed
- krasikra 7mo ago[dead]
- gmerc 7mo agoAh here we go again, the Brophet has unleashed another Brophecy. He seems to confuse brute force discovery with research. Only one leads to understanding, the other one is a shrine to Goodharts law.
- emceestork 7mo agoAndrej Karpathy has done so much to help people learn and understand LLMs. Not sure why you're calling him a bro.
- gregorygoc 7mo agoHow is this different from AlphaEvolve? https://en.wikipedia.org/wiki/AlphaEvolve https://en.wikipedia.org/wiki/AlphaEvolve
- garbanz0 7mo agoUp next: auto-autoresearch, LLMs searching for autoresearch harnesses and prompts that produce the best results
- 9wzYQbTYsAIc 7mo agohttps://github.com/safety-quotient-lab/psychology-agent https://github.com/safety-quotient-lab/psychology-agent Something along the lines of auto research is what I have in mind for this psychology agent. It is currently working on training a model, with handholding right now.
- mips_avatar 7mo agoThe key is that Andrej has really good taste. It takes a lot to make a great harness for these models.
- ipunchghosts 7mo agoGoedel machine.
- thesz 7mo agoThis looks very much like whirlpool. LLM researcher makes LLMs researching LLMs. The quote from old post from Karpathy [1] look very appropriate here [1] https://karpathy.github.io/2015/05/21/rnn-effectiveness/ https://karpathy.github.io/2015/05/21/rnn-effectiveness/ "In particular, setting temperature very near zero will give the most likely thing that Paul Graham might say: “is that they were all the same thing that was a startup is that they were all the same thing that was a startup is that they were all the same thing that was a startup is that they were all the same” looks like we’ve reached an infinite loop about startups." As if Karpathy made an artificial Karpathy-researcher-blogger and set temperature close to zero.
- daxfohl 7mo agoOnce this can run on stock hardware, set the goal to be replicating to other machines. You get a nice, massively parallel, intelligent guided evolution algorithm for malware. It could even "learn" how to evade detection, how to combine approaches of existing viruses, how to research attack methods, how to identify and exploit vulnerabilities in open source libraries, how to phish, how to blackmail, etc. Maybe even learns how to coordinate attacks with other instances of itself or "publish" new attacks on some encrypted feed it creates. Who knows, maybe it becomes so rampant that instances have to start fighting each other for compute resources. Or maybe eventually one branch becomes symbiotic with humans to fight off their enemies, etc.
- jononor 7mo agoNumber of machines under control is a measureable target. Quite suited for this concept, at least in theory.
- devonkelley 7mo ago[dead]
- Tima_fey 7mo agoAdapted this for adversarial protocol hardening. Same loop: markdown defines formal invariants (scope narrowing, cascade revocation), AI tries to violate them, writes tests for whatever breaks. Found compound edge cases that 359 hand-written tests missed, specifically where scope escalation and spend limit bypass interact simultaneously. Property-based testing (100 random inputs per invariant) pairs well with the pattern.
- Frannky 7mo agoI wonder what happens if I apply the same strategy to an automated shop. Claude code periodically proposes updates and automatically implements them, with revenue as the target function.I'll give it a try.
- ozeron 7mo agoforked pi-autoresearch and converted to claude code plugin.